dmtet|Papers with Code

dmtet,DMTet is a deep generative model that synthesizes high-resolution 3D shapes from coarse voxels or noisy point clouds. It combines implicit and explicit 3D representations using a .

Nob 8, 2021 — DMTet is a deep generative model that can synthesize high-resolution 3D shapes from coarse voxels. It combines implicit and explicit 3D representations using a .DMTet is a neural network that uses a signed distance function encoded with a deformable tetrahedral grid to generate high-resolution 3D meshes from point clouds or voxelized .DMTet is a deep 3D generative model that can synthesize high-resolution 3D shapes from coarse voxels. It combines implicit and explicit 3D representations using a deformable .DMTET is a deep 3D generative model that can synthesize high-resolution 3D shapes from coarse voxels. It uses a novel hybrid representation that combines implicit and explicit .

DMTet is a deep 3D generative model that synthesizes high-resolution 3D shapes from coarse voxels. It combines implicit and explicit 3D representations using a differentiable .

DMTet is a deep 3D generative model that can synthesize high-resolution 3D shapes from coarse voxels. It combines implicit and explicit 3D representations using a deformable .DMTet is a deep 3D generative model that can synthesize high-resolution 3D shapes from coarse voxels. It uses a novel hybrid 3D representation that combines implicit and .

Nob 8, 2021 — We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. It .We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. It marries the .

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page: https://nv-tlabs.github.io/DMTet/. 1 Introduction Fields such as simulation, architecture, gaming, and film rely on high-quality 3D content with rich geometric details and complex topology. However, creating such content requires tremendous expert human effort. It takes a significant amount of development time to create each individual .May 21, 2023 — Why DMTet. DMTet[1] is a hybrid explicit+implicit representation of a 3D geometry. On the explicit side, the object surface is in a tetrahedral-grid representation, and could be turned into mesh using Marching Tetrahedra (similar to Marching Cubes); then on the implicit side, the vertices in the tetrahedral-grid stores SDF values, and both the SDF . NVlabs/nvdiffrecIn this work, we introduce DMTet, a deep 3D conditional generative model for high-resolution 3D shape synthesis from user guides in the form of coarse voxels.In the heart of DMTet is a new differentiable shape representation that marries implicit and explicit 3D representations. In contrast to deep implicit approaches optimized for predicting sign .dmtetMeshDiffusion is a diffusion model for generating 3D meshes with a direct parametrization of deep marching tetrahedra (DMTet). Please refer to our project page for more details and interactive demos. Getting Started. Requirements. .

We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits of implicit and explicit 3D representations by leveraging a novel hybrid 3D representation. Compared to the current implicit approaches, which are trained to regress the .Peb 27, 2023 — 文章浏览阅读2.9k次，点赞3次，收藏5次。DMTet是一种深度学习版本的Marching Tetrahedra算法，用于高分辨率3D形状合成。它结合隐式和显式3D表达，优化重建表面以产生精细的几何细节。通过端到端的可微分过程，DMTet能够从点云或粗体素输入生成3D模型。损失函数包括表面对齐、对抗性和正则化损失 .Papers with Code We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits of implicit and explicit 3D representations by leveraging a novel hybrid 3D representation. Compared to the current implicit approaches, which are trained to regress the signed .

Peb 18, 2023 — DMTet Pipeline. 由左向右看，DMTet利用定義在可變形的四面體網格中的SDF來隱式表示3D物體表面。 可變形四面體網格 (Deformable Tetrehedral Grid)的好處在於 .

Hun 3, 2024 — 3D representation is essential to the significant advance of 3D generation with 2D diffusion priors. As a flexible representation, NeRF has been first adopted for 3D representation. With density-based volumetric rendering, it however suffers both intensive computational overhead and inaccurate mesh extraction. Using a signed distance field .We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits of implicit and explicit 3D representations by leveraging a novel hybrid 3D representation. Compared to the current implicit approaches, which are trained to regress the signed .dmtet Papers with Code We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits of implicit and explicit 3D representations by leveraging a novel hybrid 3D representation. Compared to the current implicit approaches, which are trained to regress the signed .Nob 8, 2021 — The core of DMTet includes a deformable tetrahedral grid that encodes a discretized signed distance function and a differentiable marching tetrahedra layer that converts the implicit signed distance representation to the explicit surface mesh representation. This combination allows joint optimization of the surface geometry and .

For handling dynamic data, we integrate a skinning mechanism with deep marching tetrahedra (DMTet) to form a drivable tetrahedral representation, which drives arbitrary mesh topologies generated by the DMTet for the adaptation of unconstrained images. To effectively mine instructive information from few-shot data, we devise a two-phase .

Nob 8, 2021 — We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits of implicit and explicit 3D representations by leveraging a novel hybrid 3D representation. Compared to the current implicit approaches, which are trained to .Nob 8, 2021 — We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits of implicit and .

We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits of implicit and explicit 3D representations by leveraging a novel hybrid 3D representation. Compared to the current implicit approaches, which are trained to regress the .

dmtet|Papers with Code

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